Roe Deer Movement

Abstract

abstract text

1 Introduction

< Movement of animals offer insights into requirements, threats, and reactions to landscape change (e.g., fragmentation) >

At a global scale mammal movement is significantly impacted by human presence (Tucker et al., 2018). Perhaps most notably are human modification to landscapes in the form of habitat destruction and transformation. Animals responses to relatively novel human dominated landscapes can be hard to predict a priori. Birds can increase their movements in response to habitat homogenisation (Tucker et al., 2019); whereas other groups, such as mammals and reptiles, broadly see reductions in movements, with vagility limited by human-created barriers (e.g., roads or fencing; Tucker et al. (2018); Jerina (2012); Jones et al. (2019)].

< Ungulates are often highly mobile, thereby getting involved in numerous human-wildlife interactions. Examples, such as road collisions and disease transfer are undesirable. >

Within mammals and reptiles, species that need to travel more for resource acquisition may be at greater risk of negative consequences of human modification on their movement. These negative consequences can be direct, such as road mortalities, or indirect, such as greater energetic costs of movement < REFS >.

Many ungulate species have been demonstrated as being vulnerable to having their movements disrupted by human action and human landscapes. < EXAMPLE HERE >.

Deer represent a key group of ungulates in Europe, having to contend with high and near comprehensive human land transformation (Mu et al., 2022). They are known to react to roads and buildings < ADD DIRECTION OF EFFECT >, with a general tendency to avoid human modification (Coulon et al., 2008).

< By understanding movement we may be better placed to predict and mitigate harmful interactions for deer and humans. >

Even in the absence of concrete information on deer movements, a myriad of management interventions are often levied at mitigating deer movements, actions, and numbers (Pepper, Barbour & Glass, 2020). Much of these efforts are informed by data concerning < CHECK PAPERS ON FENCING ETC >, that would be complimented by an examination of deer movements in areas yet to see that form of investigation such as the UK. A clear and complete picture of deer ecology would aid in tackling the controversial topic of deer management, and help reach the ultimate goals including forest regeneration and road collision mitigation, while also providing insights into key deer propagated tick-borne diseases.

In the UK, research has investigated the impacts of land use, roads, urban areas and predator removal on deer < REFS >, with a particular focus on Red Deer (Cervus elaphus) due to its commercial value. Comparatively Roe Deer (Capreolus capreolus) remain understudied in parts of the UK such as Scotland (Mitchell, Staines & Welch, 1977).

Roe deer are the smallest native deer species in the United Kingdom and the most ubiquitous, covering almost all the British mainland from the northern highlands of Scotland to southern Wessex in England (Burbaiteė & Csányi, 2009). Roe deer are ecologically and spatially flexible, able to adapt their spatial use in response to landscape, cues from conspecifics, and predator presence (Jepsen & Topping, 2004; Basak et al., 2020). Examples exist of them using isolated woodlands to outskirts major cities (Jepsen & Topping, 2004; Basak et al., 2020) < CHECK REFS >, and are capable of inhabiting mosaics of both woodland and open country, even when they include human disturbance Ewald et al. (2014).

< Roe deer are widely distributed across Europe and are known vectors for ticks as well as frequent road collision victims > < Compared to Europe, the UK populations of Roe Deer are comparatively less well documented despite the existence of the same human-deer interaction concerns. >

Here we expand the knowledge of Roe Deer movement, targeting two different landscapes in the UK. We aim to document baseline space use of UK Roe Deer, while exploring their movement in relation to various human-created land use types and landscape features, with a particular focus on roads.

2 Methods

2.1 Roe Deer Tracking

The study was conducted in two locations: Wessex, and Aberdeenshire (Fig. 2.1). Broadly, both locations represent mixed used landscapes housing patches of woodland. We employed a net fence capture method, whereby … < MORE DETAILS on capture methods >

More specifically, we focused deer capture efforts in four Aberdeenshire locations: Muir of Dinnet (2 female deer), Black Hillocks (1 male deer), Wellhouse Woods (2 female and 1 male deer), Moss of Air (2 female deer), and Gask Woods (3 female and 1 male deer). These sites were selected because < JUSTIFCATION HERE >.

• The Muir of Dinnet is a National Nature Reserve managed by NatureScot which mainly including deciduous woodland, open grassland and bogs.
• Black Hillocks is managed by the Glendye estate and includes hilly Scots Pine (Pinus sylvestris) forest, open heathland and bog.
• Wellhouse woods consist of pine woodland surrounded by farmland.
• Moss of Air consists of a mixed woodland with some open areas and bogs.
• Gaskwood is another mixed woodland site and is close to the town of Skene, Scotland.

In Wessex, we focused deer capture at Bentley Wood, Holly Hatch, and Kings Garn, due to < JUSTIFCATION HERE >.

< WESSEX DETAILS >

We captured deer using long netting from January to September of 2023 in Aberdeenshire, and between __ and __ in Wessex.

We fitted captured adult Roe Deer with GPS-collared (30mm reinforced Tellus GP Light Iridium by Followit) that weighted 276g, representing less than 10% of the deer total body mass. We set the collars provide a GPS fix every 3 hours in Aberdeenshire. In Wessex the collar fix frequency was increased to __, as a direct response to evidence of collar rubbing on the deer. This was implemented to maximise data prior to early collar removal to minimise further harm to the individuals. As a result, the Wessex dataset is more limited in overall duration.

Figure 2.1: Locations of the study sites in Aberdeenshire and Wessex. Points show the mean location of Roe Deer. Green areas in right panel depict woodland, and grey lines show roads. All maps are north orientated.

Table 2.1: Summary of tracking data used in analysis.

Deer ID	Duration (days)	Number of fixes	Fixes per day	Mean time lag between fixes (hours)
Roe01_F	252.50	1799	7.12	3.37 ±1.49
Roe02_F	255.50	1805	7.06	3.4 ±1.61
Roe03_M	49.88	305	6.11	3.94 ±1.81
Roe04_F	248.50	1787	7.19	3.34 ±1.45
Roe05_F	101.88	676	6.64	3.62 ±1.18
Roe06_F	282.50	1938	6.86	3.5 ±1.57
Roe07_F	52.92	368	6.95	3.46 ±1.67
Roe08_M	250.50	1799	7.18	3.34 ±1.42
Roe09_M	254.50	1821	7.16	3.36 ±1.44
Roe10_F	270.50	1873	6.92	3.47 ±1.6
Roe11_F	67.88	548	8.07	2.98 ±0.15
Roe12_F	139.88	1120	8.01	3 ±0.21
Roe13_F	280.50	1931	6.88	3.49 ±1.53
Roe14_M	149.88	1201	8.01	3 ±0.18
Roe15_F	282.50	1932	6.84	3.51 ±1.59
Note:
Deer ID suffix denote the sex of the individual deer.

We retrieved movement data from 15 GPS collars worn by Roe Deer, 13 in Aberdeen, 2 in Wessex. We re-sampled the Roe Deer data to a more consistent rate, aiming for a standard 3 hour time lag between locations (with a 1 hour tolerance). We additionally filtered out the first week’s worth of data to avoid the impacts of capture/immediate post-release movements that may have been atypical.

2.2 Home Range Estimation

We estimated roe deer home range using autocorrelated kernel density estimators (Fleming et al., 2015; Calabrese, Fleming & Gurarie, 2016; Fleming & Calabrese, 2017, 2023). This process consisted of fitting a number of continuous time movement models to an individuals movement data, selecting the best fitting movement model, and extracting a suitable range contour from the resulting utilisation distribution. We fit the following models (following the default process provided by the ctmm package): Ornstein-Uhlenbeck (OU), Ornstein–Uhlenbeck Foraging (OUF), and Independent Identically Distributed (IID), all in both isotropic and anisotropic forms. We elected to use perturbative hybrid residual maximum likelihood (pHREML) and (Fleming et al., 2019; Silva et al., 2022) AICc to determine the best fitting movement model on an individual basis, and used that single best fitting model for all further estimations.

Before committing to the estimations of home range size we examined whether the roe deer exhibited stable ranges through the visual inspection of variograms. A stable range should be reveal by a clear asymptote in the variogram. We paired these visual inspections with a judgement of effective sample size to help gauge our confident in the area estimates [effective sample size approximating the overall tracking duration divided by the mean time taken to cross the home range; Silva et al. (2022)]. All our individuals showed effective sample sizes between 154.4 and 926.7, leading us to be confident in overall home range estimates.

Having determine the data suitability for home range estimations, we extracted the 95% and 99% contours from the weighted AKDE estimate, alongside 95% CI surrounding that contour. We selected 95% as a balance between a generous estimate of home range, while also avoiding the undue influence of the most extremely outlying movements. To generate an overall home range estimate for Aberdeen roe deer, we averaged the home ranges using the weighted mean function provided by the ctmm package (Silva et al., 2022; Fleming & Calabrese, 2023). This way the home range mean is weighted by the confidence (i.e., ESS) surrounding each home range.

We retained 99% estimates help quantify the range at which Roe Deer will range away from woodland patches. We calculated the widest dimension of each 99% home range polygon (or largest polygon if the home range area was non-contiguous), and halved that value to quantify the distance deer would be willing to travel beyond their resident patch. To support this approach, we determined the distance from patch for every deer location that fell outside a patch.

2.3 Habitat Selection

We used the reformulated Poisson model approach described by Muff, Signer & Fieberg (2020) to generate estimates of habitat selection as well as gauge deer’s movement capabilities in relation to different aspects of the landscape.

The model required data pertaining to the used locations (i.e., GPS locations of the deer) and comparable data on randomly generated available points (i.e., randomly generated locations the deer could that travelled). For each confirmed deer location we generated 10 random alternative locations they could have travelled to. The location of these random locations was governed by two distributions. A Gamma distribution that random step lengths were drawn from, and a Von Mises distribution that random turn directions were drawn from. Both distribution where calibrated (e.g., shape, size, mu, and kappa) by the underlying movement data.

Once all random locations had been generated we extracted a suite of environmental conditions at all those locations. First was the land use type as described by the 2023 UKCEH land cover maps, which is a 25m resolution classified raster originally based on Sentinel-2 imagery (Morton et al., 2024). Validation of these data suggest 83% accuracy (Morton et al., 2024). The UKCEH land cover data comprises of 21 land cover classes, broadly following the Biodiversity Broad Habitats (Jackson, 2000).

We recategorised these land use types categories into 10 more general categories that reduced instances of limited interaction with the deer movement data thereby aiding habitat selection model convergence and avoided extreme, unstable selection estimates.

In addition to land use types, we also acquired woody linear feature (i.e., hedgerows) data from UKCEH (Scholefield et al., 2016). This dataset maps the woody linear features across the UK (e.g., woodland) as polylines, based on Ordnance Survey maps and the 2007 UKCEH Land Cover Map (Morton et al., 2011).

We converted the polygon spatial data into a raster, where 1 == hedgerow, and used that rasterisation to generate a distance to hedgerow raster for the entire study landscape. We conducted the same process to create a distance to woodland raster, where we calculated the distance from any area the UKCEH land use data classed as deciduous or coniferous woodland. These distance rasters allowed for easy extraction of the distance to the nearest hedgerow and woodland for all locations.

We acquired road data from OS map open GOV licensed (Ordnance Survey, 2024). We created a binary variable describing crossing events for all steps, with all steps that crossed one or more of the roads being classed as 1. This binary variable allowed us to estimate the likelihood deer cross a road and therefore the level of barrier roads present.

To ensure compatibility between all data sources, we ensured all data was projected into the British National Grid (BNG) coordinate reference system (OSGB36, epsg: 27700) before undertaking analysis.

The Poisson model formulae consisted of land use (a 8-term category variable formed into 7 dummy variables, with deciduous woodland placed as the reference category, barren and other excluded), distance to woodland (continuous in m), distance to hedgerow (continuous in m), road crossing (binary). In addition to these selection focused predictors, we several movement predictors including step length, log step length, and cos turn angle, as well as the interaction between step length and log of step length with all land use types. As the population Possion model contained all individuals the formula required in the inclusion of fixed Gaussian processes accounting for the time step as well as the individual. This formulation, namely the fixed Gaussian processes, as described by Muff, Signer & Fieberg (2020) allows for the efficient estimation of population level selection using integrated nested Laplace approximation (INLA) (Martins et al., 2013; Lindgren & Rue, 2015)

To supplement the population model, we ran individual step-selection models for all deer. These models used the same data as the Poisson model. For the individual models, we used a formula that included landuse type, distance to woodland, distance to hedges, a binary describing whether they crossed a road, step length, log step length, and cos of turn angle. We used the IndRSA package (Bastille-Rousseau, 2025) to explore the variation of resulting coefficients, specifically specialisation, heterogeneity, and a weighted population mean (Bastille‐Rousseau & Wittemyer, 2022). Specialisation is the absolute magnitude of the coefficients; differences compared to the population mean coefficient could highlight diverging responses to the habitat covariates. This can be particularity informative when the diverging responses have resulted in a “neutral” population mean for the coefficient. Heterogeneity is the standard deviation of the coefficients; therefore, larger values indicate greater variation in the response. To carry forward the uncertainty surrounding the initial habitat coefficients, 10000 replicates of each metric were generated from a normal distribution centred on the original coefficient with a standard deviation equal to the standard error of the coefficient (Bastille‐Rousseau & Wittemyer, 2022).

3 Results

Our GPS collaring of Roe deer resulted in 20903 location fixes, with a mean of 1394 SD±627.9 per individuals, spread across a mean of 196 SD±90.99 days per individual (Fig. 6.1). Overall this resulted in an average of 7.133 SD±0.5359 fixes per day per individual (Fig. 6.2; Tab. 2.1).

For our Aberdeen Roe Deer home range sizes ranged from 32.2 to 122.5ha (95% contour point estimates), with a weighted mean of 65.3 ha (95% CI 55.5-75.8) (Fig. 3.1). Largely the deer appeared range resident; however, a couple of individuals may show evidence of a range shift during the tracking period (Roe Deer 8, Roe Deer 3; Fig. 3.2). All bar two individuals found OU models to fit best, with the remaining two being better described by OUF models. All best fitting models were anisotropic, suggesting these Roe Deer are inhabiting non-uniform home ranges (i.e., not being as wide as they are tall). The distribution of the distance to woodland values that revealed that 95% of all deer movements fell within the mean of half longest dimension of the 99% home range area (756 m; Fig. 3.3)).

Figure 3.1: Home range size as estimated via the Autocorrelated Kernel Density Estimators. Depicted are the 95% contour with 95% confidence intervals surrounding that estimate. The vertical line shows the naive mean of all home range estimates.

Figure 3.2: Variograms showing the autocorrelative structure of the Roe Deer movement data, revealing the level of range residency each individual displayed.

Figure 3.3: Distribution of the distances deer locations were from patches when outside of a patch.

The Poisson habitat selection model reveal a general tendency for Roe Deer to remain closer to the woodland patches (-0.0051; 95% CI -0.0082 to -0.0022), and with no significant care for hedgerows (-9e-04; 95% CI -0.0019 to 1e-04; Fig. 3.4). The model also revealed that roads play a significant role in reducing connectivity across the landscape, with a significantly negative coefficient (-0.76; 95% CI -1.2 to -0.36).

Overall selection revealed significant selection for open shrubland (1.5; 95% CI 0.62 to 2.2) and tall grassland (0.57; 95% CI 0.17 to 0.98), with other relationships being less clear. Movement was most impacted by short grassland, tall grassland, and cropland, all showing the same pattern. These step lengths in these land uses to be lower, as seen in coefficients for log step length (short grassland -0.76; 95% CI -1.2 to -0.32; tall grassland -0.18; 95% CI -0.24 to -0.12; cropland -0.16; 95% CI -0.24 to -0.078) but with a larger tail to the gamma distribution (i.e, larger coefficient for step lengths (short grassland 0.0044; 95% CI 0.0018 to 0.007; tall grassland 0.00048; 95% CI 5.1e-05 to 0.00092; cropland 0.0012; 95% CI 0.00064 to 0.0017).

Estimates regarding human settlements were paired with very wide confidence intervals (-70; 95% CI -180 to 36), likely a result of minimal interaction with the tracked deer making estimation difficult. There are therefore treated as the point estimate suggests as areas of very low conductivity in further analysis.

Figure 3.4: Coefficient estimates, with 95% confidence intervals, from the Poisson model of habitat selection. Major outlying estimates have been replaced with labels to aid visualisation. Colour highlights and bolding reflect the significantly negative (light orange) and significantly positive (dark orange) coefficients.

Further exploration of individual habitat selection models highlights whether the uncertainty in the Poisson model stems from weak responses or diverging responses that average towards a zero effect. Distance to woodland shows a marginally higher specialisation (0.01) compared to the population mean (-0.01; 95% CI -0.01 to 0; Fig. 3.5). This difference can almost certainly be entirely explained by the deviating Roe 10 who expressed an opposite response to the majority of other deer (0.01 ±0). Outside of Roe 10, Roe Deer are showing the same clear preference for remaining near woodland as seen in the population Poisson model. Roe 10 is also likely the reason the estimated heterogeneity in response to distance to woodland (0.0134) is greater than distance to hedges (0.0019; Fig. 6.3).

Distance to hedges does not see the same consistent response, instead with a number of individuals showing a opposing responses. This is reflected in the population mean being close to zero (0; 95% CI 0 to 0), while the specialisation is considerably greater (0; Fig. 6.4). The combination of which could indicate two differing responses to hedges in the sampled Roe Deer.

The chance of crossing a road is considerably more consistent, with the vast majority of individuals more opted to avoid crossing roads; this is reflected in a significantly negative population mean (-0.51; 95% CI -0.81 to -0.22). The elevated specialisation (134.97) and heterogeneity (417.65) values are almost entirely driven by two very uncertain estimates from Roe 09 (17.96 ±888.36) and Roe 11 (-17.99 ±1620.8). Other than those individuals, we can be confident in a consistently negative response to road crossing in Roe Deer.

Land use variables presented the hardest to obtain confident estimates for due to the variable exposure of different individuals to each type. Cropland and Evergreen Needleleaf Forest showed the high rates of significant estimates, and both population estimates (-0.19; 95% CI -0.56 to 0.18; -0.07; 95% CI -0.31 to 0.17) matched with the results from the Poisson model (-0.22; 95% CI -0.99 to 0.52; -0.37; 95% CI -1 to 0.25). In both cases a single individual revealed a strong but very uncertain negative response that can explain the heterogeneity (353.18; 252.83) and specialisation (107.22; 68.37) values. The response to Evergreen Needleleaf Forest appears the least consistent, with multiple individuals expressing significantly negative and positive responses to the landuse. Tall Grassland showed similar levels of diverging estimates, with individuals showing a mix of positive and negative responses. Unlike the population estimates for Cropland and Evergreen Needleleaf Forest, the population mean for Tall Grassland (-0.3; 95% CI -0.57 to -0.03) does not match the Poisson model (0.57; 95% CI 0.17 to 0.98). This may be indicative that the interaction effects included in the Poisson model are mediating the responses to Tall Grassland. A similar reason may explain the clear Open Shurbland response in the Poisson model 1.5; 95% CI 0.62 to 2.2 that is absent in the individual models and the population mean 0.03; 95% CI -0.63 to 0.69; however, this is more likely to be caused by the limited number of individuals exposed to Open Shrubland and a different handling of Roe 03’s strongly negative response. The other landuse types are all harder to confidently interpret given the frequency of very uncertain extreme estimates. The uncertainty surrounding Human Settlements and Permanent Wetland is well reflected in the Poisson model. The high levels of specialisation and heterogeneity are driven by these same extreme estimates. The lack of exposure to these landuse types and potentially inconsistent response leaves the a lot of uncertainty concerning Roe Deer response to these landuse types.

Figure 3.5: Coefficient estimates for all individual step-selection models, along side population averages per covariate. Error bars with the population estimates are the 95% confidence interval. Colour highlights reflect the estimates whose standard errors do not overlap zero, either negatively (light orange) and positively (dark orange).

4 Discussion

Our tracking of 13 Roe Deer revealed that they have limited home ranges, that are heavily skewed towards deciduous woodland. They are willing to exit woodland, making use of open shrubland and grasslands, but these movements tend to be limited to within ~750m. When entering these more open non-wooded areas they tend to slow down. Individual models also highlight the consistency of the woodland preference. However, the responses to landuse types was variable between individuals, and their limited exposure to certain landuse types (e.g., human settlement) suggests more study to required to fully characterise UK Roe Deer habitat selection. Overall, Roe Deer exhibited a disinclination to cross roads found on a population and individual level. The documented chances of individuals crossing roads indicate that roads reduce the permeability of these landscapes for Roe Deer.

< contextualise against European findings – Home range size - Can use home range database for quick overview >

Our findings on Roe Deer home range size are remarkably similar to those values reported in the HomeRange database for Roe Deer, with the database presenting examples of ranges larger and smaller [Broekman et al. (2023); Broekman et al. (2022); see references in data availability section]. A naive mean of those reviewed values suggests a range of 62 ha, that compares well to our 65 ha. This coherence is surprising given the rudimentary nature of the comparison, that does not account for differences in sampling protocol, duration, or home range estimation method. As such the UK Roe Deer examined here appear largely typical in regards to their use of space.

< contextualise against European findings – preference >

< STRANGE framework paragraph > There are several aspects of the study that may limit its generalisability, or that would need to be considered when applying the findings to other contexts. Using the STRANGE framework (Webster & Rutz, 2020), we highlight key limitations (omitting those that are unquantifiable or of limited relevance). Social background. We have little information on the individuals not tracked that may be impacting the movements of tracked deer. Roe Deer will maintain territories , so it is likely that the interactions with conspecifics could alter the distribution and size of our tracked deer’s movements via competitive exclusion or territorial patrolling. Trappability and self-selection. While there is not obvious bias to the deer trapping methods we used, there may be an unknown behavioural variation altering the likelihood of a deer being captured. If the trappability of a deer is associated with certain movement or behavioural parameters, our sample may be skewed towards those tendencies (e.g., boldness leads to increased capture likelihood, while also being connected to increased chance of crossing roads). Acclimation and habituation. None of the deer had been previously collared, but there may have been a habituation effect to collars over time. Our removal of the first week of data likely have mitigated the largest impact prior to collar-habituation, but as the Wessex deer demonstrate there may be ongoing effects we cannot control for.

< implications for management? > < road collisions reduced by or are reducing deer likelihood to cross roads? >

< conclusions / take aways >

Overall, we documented a strong preference for woodland in UK Roe Deer that apparently limits their ranges to areas within 750m of woodland patches. However, the resulting home ranges of UK Roe Deer do not appear atypical when compared to other tracked Roe Deer. We see a clear and consistent, albeit slight, aversion to crossing roads that may be limiting the permeability of the landscape for Roe Deer. Questions remain regarding their responses to certain landuse types that were not adequately represented in areas traversed by the deer. Further work could benefit from focusing on UK Roe Deer in areas with a greater urban footprint, or comparing movements against more granular quantifications of human activity (Gomez et al., 2025).

5 Acknowledgements

5.1 Software availability

For all analysis we used R (v.4.4.2) (R Core Team, 2024), and R Studio (v.2024.12.0+467) (Posit team, 2024). For analysis of animal movement data we used amt (v.0.2.2.0) (Signer, Fieberg & Avgar, 2019), ctmm (v.1.2.0) (Fleming & Calabrese, 2023), and move (v.4.2.6) (Kranstauber, Smolla & Scharf, 2024). For general data manipulation we used glue (v.1.8.0) (Hester & Bryan, 2024), sjmisc (v.2.8.10) (Lüdecke, 2018), tidyverse (v.2.0.0) (Wickham et al., 2019), and units (v.0.8.5) (Pebesma, Mailund & Hiebert, 2016). For project and code management we used here (v.1.0.1) (Müller, 2020), tarchetypes (v.0.11.0) (Landau, 2021a), and targets (v.1.9.0) (Landau, 2021b). For visualisation we used the following as expansions from the tidyverse suite of packages: ggdist (v.3.3.2) (Kay, 2024a,b), ggridges (v.0.5.6) (Wilke, 2024), ggtext (v.0.1.2) (Wilke & Wiernik, 2022), patchwork (v.1.3.0) (Pedersen, 2024), and scales (v.1.3.0) (Wickham, Pedersen & Seidel, 2023). Other packages we used were boot (v.1.3.31) (A. C. Davison & D. V. Hinkley, 1997; Angelo Canty & B. D. Ripley, 2024), circular (v.0.5.1) (Agostinelli & Lund, 2024), doParallel (v.1.0.17) (Corporation & Weston, 2022), foreach (v.1.5.2) (Microsoft & Weston, 2022), knitr (v.1.49) (Xie, 2014, 2015, 2024), and usethis (v.3.0.0) (Wickham et al., 2024). To generate typeset outputs we used bookdown (v.0.42) (Xie, 2016, 2025), and rmarkdown (v.2.29) (Xie, Allaire & Grolemund, 2018; Xie, Dervieux & Riederer, 2020; Allaire et al., 2024). To manipulate and manage spatial data we used gdistance (v.1.6.4) (van Etten, 2017), raster (v.3.6.30) (Hijmans, 2024a), sf (v.1.0.19) (Pebesma, 2018; Pebesma & Bivand, 2023), sp (v.2.1.4) (Pebesma & Bivand, 2005; Bivand, Pebesma & Gomez-Rubio, 2013), terra (v.1.7.83) (Hijmans, 2024b), and tidyterra (v.0.6.1) (Hernangómez, 2023). To run models and explore model outputs we used effects (v.4.2.2) (Fox, 2003; Fox & Hong, 2009; Fox & Weisberg, 2018, 2019), INLA (v.24.6.27) (Martins et al., 2013; Lindgren & Rue, 2015), lme4 (v.1.1.35.5) (Bates et al., 2015), and performance (v.0.12.4) (Lüdecke et al., 2021).

5.2 Data availability

< Where is the data going: movebank and somewhere else >

Studies that the HomeRange Database mean was based on: Melis, Cagnacci & Lovari (2005); Biosa et al. (2015); Dupke et al. (2017); Rossi et al. (2003); Focardi et al. (2006); Picardi et al. (2019); Ramanzin, Sturaro & Zanon (2007); Mysterud (1999); Ranc et al. (2020); Richard et al. (2008); Aiello, Lovari & Bocci (2013); Morellet et al. (2013); Vanpé et al. (2009); Pellerin et al. (2016); Kjellander et al. (2004); Van Laere, Boutin & Gaillard (1996); Cederlund (1983); Saïd et al. (2005); Bideau et al. (1993); Cimino & Lovari (2003); Lamberti et al. (2001); Lamberti et al. (2006); Bevanda et al. (2015); Pagon et al. (2017); Debeffe et al. (2012); Chapman et al. (1993); Lamberti, Mauri & Apollonio (2004); Maublanc et al. (2018); Padié et al. (2015); Saïd & Servanty (2005); Carvalho et al. (2008); Malagnino et al. (2021); Linnell & Andersen (1995); Rossi et al. (2001); Jeppesen (1990).

5.3 Author Contributions

6 Supplementary Material

Figure 6.1: Dates of data collection and overall duration or deer tracking by individual.

Figure 6.2: Distribution of time lags after resampling and filtering. N.b. x axis is log scaled.

Figure 6.3: The simulated heterogeneity (standard deviation) values from individual coefficients and standard errors of the step-selection models.

Figure 6.4: The simulated specialisation (absolute coefficients) values from individual coefficients and standard errors of the step-selection models.

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